Implementation Details:
During training, we train the above models except GLN with the same configuration.
The batch size is \(32\times 4\times 30\) , where 32 denotes the number of IDs, 4 denotes the number of training samples per ID, and 30 is the sequence length.
The models are trained for 1,200 epochs with the initial Learning Rate (LR)=1e-3 and the LR is multiplied by 0.1 at the 200-th and 600-th epochs.
The optimizer is Adam [1]} and the weight decay is set to 5e-4.
For GLN, we follow the two-stage training as in [2]}.
The model trained in the first stage is used as the pre-trained model for the second stage.
Both of the two stages are trained with the same configuration of other methods.
During testing, we use the cosine similarity to measure the similarity between each pair of query and gallery sequences.
For the GaitSet, GaitPart, GLN, and GaitGL models, we adopt the implementations in the open-source OpenGait toolbox https://github.com/ShiqiYu/OpenGait since they outperform the original codes.
